Development and application of a genetic algorithm-based tool for the reduction and optimization of reaction kinetic mechanisms

An automatic method for the reduction and optimization of chemical kinetic mechanisms under specific physical or thermodynamic conditions has been developed and described in this work. The mechanism reduction method relies on the genetic algorithm (GA) search for a smallest possible subset of reactions from the detailed mechanism while still preserving the ability of the reduced mechanism to describe the overall chemistry at an acceptable error. Accuracy of the reduced mechanism is determined by comparing its solution to the solution obtained with the full mechanism under the same initial and/or physical conditions. For the reduction, not only the chemical accuracy and the size of the mechanism are considered but also the time for its solution which helps to avoid stiff and slow-converging mechanisms.
The (subsequent) optimization technique is based on a genetic algorithm that aims at finding new reaction rate coefficients to restore the accuracy which is usually decreased by the preceding reduction process. The accuracy is defined by an objective function that covers regions of interest where the reduced mechanism may deviate from the original mechanism. The objective function directs the search towards more accurate reduced mechanisms that are valid for a given set of operating conditions. The mechanism's performance is assessed for homogeneous-reactor or laminar-flame simulations against the results obtained from a given reference.
An additional term introduced to the objective function is a so-called penalty term that influences the reaction rates during the optimization. With the penalty term, the change to the reaction rates can be minimized, keeping them as close as possible to their nominal values. It is demonstrated that the penalty function can be used instead of defining the uncertainty bounds from the literature for each reaction in the mechanism, which can be a tremendous effort when dealing with large or insufficiently investigated mechanisms. The penalty term can also be used for further reduction of the mechanism by driving the reaction rates towards zero during the optimization. This approach is addressed in a greater detail in the final section of the thesis which shows the convergence behaviour of the integer-coded reduction, the real-coded optimization and reduction of the reduced mechanisms and the real-coded-optimization and reduction of the full mechanism. The convergence study shows that the real-coded optimization with the size-penalty function exhibits the fastest convergence towards one global optimum, which makes a good case for investigating and improving the real-coded reduction as a direct way to optimize and reduce the full mechanism at the same time.
The GA-based reduction and optimization method has shown to be robust, flexible, and applicable to a range of operating conditions by using multiple criteria simultaneously.

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